This preprint has been published.
DOI: 10.11409/mit.42.15
Preprint / Version 1

Deep learning for PET image reconstruction

some current approaches

##article.authors##

  • Fumio Hashimoto Central Research Laboratory, Hamamatsu Photonics K.K.
  • Reader, Andrew School of Biomedical Engineering and Imaging Sciences, King’s College London

DOI:

https://doi.org/10.51094/jxiv.615

Keywords:

PET image reconstruction, deep learning

Abstract

Deep learning has long been applied to PET image reconstruction. In this brief review, we first cover a brief history of PET image reconstruction as well as a straightforward explanation of deep learning-based PET image reconstruction, from the basic ideas of deep learning reconstruction to some of the most recent research developments.

Conflicts of Interest Disclosure

There is no conflict of interest.

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References

［1］ Chu G, K. Tam KC: 3-Dimensional Imaging in Positron Camera Using Fourier Techniques. Phys Med Biol 22: 245, 1977

［2］ Reader AJ, et al: Intercomparison of four reconstruction techniques for positron volume imaging with rotating planar detectors. Phys Med Biol 43: 823, 1998

［3］ Shepp LA, Vardi Y: Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging 1: 113-122, 1982

［4］ Levitan E, Herman GT: A Maximum a Posteriori Probability Expectation Maximization Algorithm for Image Reconstruction in Emission Tomography. IEEE Trans Med Imaging 6: 185-192, 1982

［5］ Mehranian A, et al: PET image reconstruction using multi-parametric anato-functional priors. Phys Med Biol 62: 5975, 2017

［6］ Zhou B, et al.: Image reconstruction by domain-transform manifold learning. Nature 555: 487-492, 2018

［7］ Häggström I, et al.: DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal 54: 253-262, 2019

［8］ Mehranian A, Reader AJ: Model-based deep learning PET image reconstruction using forward–backward splitting expectation–maximization. IEEE Trans Radiat Plasma Med Sci 5: 54-64, 2021

［9］ Reader AJ, Pan B: AI for PET image reconstruction. Br J Radiol 96: 1330-1333, 2023

［10］ Gong K, et al.: PET image reconstruction using deep image prior. IEEE Trans Med Imaging 38: 1655-1665 2019

［11］ Hashimoto F, Ote K, Onishi Y: PET image reconstruction incorporating deep image prior and a forward projection model. IEEE Trans Radiat Plasma Med Sci 6: 841-846, 2022

［12］ Hashimoto F, et al.: Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm. Phys Med Biol 68: 155009, 2023

［13］ Ote K, et al: List-Mode PET Image Reconstruction Using Deep Image Prior. IEEE Trans Med Imaging 72: 1822-1834 2023

［14］ 橋本二三生，他：Deep learning PET画像再構成への招待Med Imag Tech Under review

［15］ Reader AJ, et al: Deep learning for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci 5: 1-25, 2020

［16］ Hashimoto F, et al: Deep learning-based PET image denoising and reconstruction: a review. Radiol Phys Technol, 2024. doi: 10.1007/s12194-024-00780-3

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Submitted: 2024-02-02 01:53:41 UTC

Published: 2024-02-07 01:05:17 UTC
Section
Information Sciences

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Copyright (c) 2024 Fumio Hashimoto
Reader, Andrew

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.